Software Engineer: Machine Learning / Signal Processing

Title: Software Engineer: Machine Learning / Signal Processing

Location: Washington, DC or Culpeper, VA or new Northeast location

Applied Research in Acoustics (ARiA) applies broad interdisciplinary expertise in acoustics, modeling & simulation, signal processing, and cognitive science toward innovative science and engineering research and development for a diverse set of government and corporate clients that focuses on modeling & simulation for training and model-based signal-processing and artificial intelligence for detection and classification. Partnering with government, industry, and academia, ARiA solves critical challenges.

To enable effective and efficient transition of applied research to advanced development of products and systems, ARiA is structured to bring together top-quality research scientists with development and software engineers in an environment in which research and development mutually benefit from joint leveraging of in-house expertise.

ARiA is a small business where you can make a big difference. Our employees, including those at entry-level, are working together with the CIO of the Air Force to use cognitive computing to change the way the government acquires technology and working with top Navy leaders to use video games and simulation to change the way sailors are trained to use sonar.

Software engineer to perform a variety of tasks including development and implementation of machine-learning algorithms and software, and design, development, and testing of machine-classification and cognitive systems, working in close coordination with ARiA scientists and engineers

Responsible for:

Algorithm and software design, development, research, and testing to support prototypes and products

Supporting the transition of research algorithms to fielded prototypes

Preparing documentation to summarize design and status of prototypes and products

Assisting with in-field integration, testing, and support, with some local travel required

Sample tasks:

Using deep learning to understand the relevant physical features in acoustic scattering data for use in a project on remediating underwater sites contaminated by unexploded ordnance

Developing a cognitive tool that allows natural-language query of legal documents to answer user questions about government regulations

Developing an ontology-based expert system to suggest scenario designs for training and performing knowledge engineering to encode representations/models